This paper offers a system for an electric vehicle. It consists of digitally controlling an induction motor without using a speed sensor. The machine is powered by a five-level cascading H-bridge inverter. The SVM control principle is used to manage the status of the five-level inverter; this removes harmonics. The H-bridge inverter converter is powered by photovoltaic sources via a serial converter, using the maximum power point tracker control principle. This structure can also reduce shading losses. In the absence of a mechanical sensor, a dynamic model of the asynchronous machine is utilized with the state variables defined in the stator reference frame. The state vector consists of the components of the rotor flux and stator current. The article provides a comparison of two methods widely used on an induction motor drive. The adaptive model-reference system method and Luenberger observer are evaluated using an active control strategy to reject disturbances to minimize the impact of disturbances. The operating principles of each method are described, and the mathematical models of training systems are developed. Both methods provide a promise for high-speed estimate applications in simulation environments. The simulation results obtained show the correct operation of both observers. Perfect decoupling between the velocity and flow control loops is observed, taking into account any disturbances that may affect the system.

中文翻译：

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使用 SVM 方法和光伏发电机对由多电平转换器驱动的无传感器感应电机进行 ADRC 和 IFOC 控制

本文提供了一种电动汽车系统。它包括在不使用速度传感器的情况下对感应电机进行数字控制。该机器由五电平级联H桥逆变器供电。采用SVM控制原理对五电平逆变器的状态进行管理；这消除了谐波。 H桥逆变转换器由光伏电源通过串行转换器供电，采用最大功率点跟踪器控制原理。这种结构还可以减少遮阳损失。在没有机械传感器的情况下，异步电机的动态模型与定子参考系中定义的状态变量一起使用。状态矢量由转子磁通和定子电流的分量组成。本文对感应电机驱动中广泛使用的两种方法进行了比较。使用主动控制策略来评估自适应模型参考系统方法和 Luenberger 观测器，以抑制干扰，从而最大限度地减少干扰的影响。描述了每种方法的操作原理，并开发了训练系统的数学模型。这两种方法都为模拟环境中的高速估计应用提供了前景。获得的模拟结果表明两个观察者的操作都是正确的。考虑到可能影响系统的任何干扰，观察到速度和流量控制回路之间的完美解耦。